Summary
Statistical regression models are a staple of predictive forecasts in a wide range of applications. In this episode Matthew Rudd explains the various types of regression models, when to use them, and his work on the book "Regression: A Friendly Guide" to help programmers add regression techniques to their toolbox.
Announcements
- Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
- When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
- Your host as usual is Tobias Macey and today I’m interviewing Matthew Rudd about the applications of statistical modeling and regression, and how to start using it for your work
Interview
- Introductions
- How did you get introduced to Python?
- Can you start by describing some use cases for statistical regression?
- What was your motivation for writing a book to explain this family of algorithms to programmers?
- What are your goals for the book?
- Who is the target audience?
- What are some of the different categories of regression algorithms?
- What are some heuristics for identifying which regression to use?
- How have you approached the balance of using software principles for explaining the work of building the models with the mathematical underpinnings that make them work?
- What are some of the concepts that are most challenging for people who are first working with regression models?
- What are the most interesting, innovative, or unexpected ways that you have seen statistical regression models used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on your book?
- What are some of the resources that you recommend for folks who want to learn more about the inner workings and applications of regression models after they finish your book?
Keep In Touch
Picks
Links
